Radiation therapy planning using integrated model

ABSTRACT

System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

CROSS REFERENCE

The present application is a continuation application of U.S. patentapplication Ser. No. 14/039,920, filed on Sep. 27, 2013, entitled“RADIATION THERAPY PLANNING USING INTEGRATED MODEL,” which claimspriority to U.S. Provisional Patent Application No. 61/793,655, entitled“PROTECTING ACHIEVABLE DOSE USING HIERARCHIAL KNOWLEDGEBASED MODELS,”filed on Mar. 15, 2013. The foregoing patent applications are herebyincorporated by reference in their entireties for all purposes.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to medicaldevices, and more particularly, to radiation therapy devices.

BACKGROUND

In knowledge-based medical treatment planning, the information ofexisting plans can be used to make a treatment plan for a new patient,e.g., by estimating an achievable dose distribution. A predication canbe made by distilling patient geometry and dose information of multipleprevious clinical plans into a prediction model that can be used fordose prediction without storing all information from the original set ofplans.

Such a knowledge-based model could have various implementations. Forexample, it could be a regression model associating geometric parametersto dosimetric parameters. Typically a certain model derived from atraining set only has a limited region, e.g., with respect to geometricparameters of a tumor, in which its predictions are valid. If thegeometric parameters of the new case differ too much of the geometricparameters spanned by the training set, the dose predictionsunfortunately can become unreliable.

A clinic usually has several predictive models that collectively cancover a large variety of different regions. Conventionally, a therapyexpert, e.g., an oncologist, has to manually explore the availablemodels and thereby determine one for prediction computation based on apersonal judgment. This manual selection process can be time consumingand possibly unreliable, especially when the number of available modelsis large, and each model corresponds to a complicated geometricparameter set.

SUMMARY OF THE INVENTION

Therefore, it would be advantageous to provide a therapy planningmechanism that can make valid predictions over extended regions in anautomated and systematic manner.

Accordingly, embodiment of the present disclosure employs an integratedmodel that combines multiple predictive models from which a resultantpredictive model can be selected automatically to perform prediction fora new patient case. The multiple predictive models are trained fromexisting clinical data and cover varieties of valid or effective regionswith respect to patient data pertaining to radiation therapy. Theintegrated model comprises a model selection module and an individualpredictive model module. The model selection module may operate toevaluate prediction results generated by each predictive model and theassociated prediction reliabilities and thereby selectively output oneor more satisfactory predictions. Alternatively, the model selectionmodule may comprise a decision hierarchy in which each level correspondsto divisions of a patient data feature set. The divisions on asubordinate level are nested with divisions on a superordinate level.The integrated model may be generated automatically, e.g., byconfiguring multiple models from a single training dataset in accordancewith a hierarchical clustering algorithm. Therefore, the integratedmodel advantageously can cover extended effective regions for therapyprediction without the need for manual selection of a suitable model.

In one embodiment of the present disclosure, a computer implementedmethod of automatically generating a radiation treatment plan for apatient comprises: (1) accessing patient information pertaining to aradiation treatment for a patient; (2) automatically selecting one ormore predictive models based on the patient information in accordancewith a hierarchical model comprising a plurality of predictive modelsarranged in a hierarchy, wherein each of the predictive model isestablished based on training data and operable to generate a radiationtreatment prediction; (3) processing the patient information inaccordance with the one or more predictive model; and (4) outputting oneor more radiation treatment predictions.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the present invention, asdefined solely by the claims, will become apparent in the non-limitingdetailed description set forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be better understood from areading of the following detailed description, taken in conjunction withthe accompanying drawing figures in which like reference charactersdesignate like elements and in which:

FIG. 1 is a function block diagram illustrating an exemplaryconfiguration of an automated therapy planning system including acollection of predictive sub-models and a model selection module inaccordance with an embodiment of the present disclosure

FIG. 2 is a flow chart illustrating an exemplary method of automatictherapy planning in accordance with an embodiment of the presentdisclosure.

FIG. 3A is a diagram illustrating an exemplary configuration of anintegrated model configured to determine a suitable predictive model byvirtue of evaluation in accordance with an embodiment of the presentdisclosure.

FIG. 3B is a flow chart depicting an exemplary method of automatictherapy planning by employing an integrated model in which a suitablepredictive model can be determined by virtue of evaluation in accordancewith an embodiment of the present disclosure.

FIG. 4A is a diagram illustrating an exemplary configuration of anintegrated model including a hierarchy of decision levels for predictivemodel selection in accordance with an embodiment of the presentdisclosure.

FIG. 4B is a flow charge depicting an exemplary method of selecting asuitable predictive model in a hierarchical model in accordance with anembodiment of the present disclosure.

FIG. 5 is a function block diagram illustrating an automated integratedmodel generation system in accordance with an embodiment of the presentdisclosure.

FIG. 6 is a flow chart depicting an exemplary method of automaticallygenerating an integrated model for therapy planning in accordance withan embodiment of the present disclosure.

FIG. 7 is a block diagram illustrating an exemplary computing systemincluding an automatic integrated model generator and an automatictherapy plan generator in accordance with an embodiment of the presentdisclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings. While the invention will be described in conjunction with thepreferred embodiments, it will be understood that they are not intendedto limit the invention to these embodiments. On the contrary, theinvention is intended to cover alternatives, modifications andequivalents, which may be included within the spirit and scope of theinvention as defined by the appended claims. Furthermore, in thefollowing detailed description of embodiments of the present invention,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. However, it will be recognizedby one of ordinary skill in the art that the present invention may bepracticed without these specific details. In other instances, well-knownmethods, procedures, components, and circuits have not been described indetail so as not to unnecessarily obscure aspects of the embodiments ofthe present invention. Although a method may be depicted as a sequenceof numbered steps for clarity, the numbering does not necessarilydictate the order of the steps. It should be understood that some of thesteps may be skipped, performed in parallel, or performed without therequirement of maintaining a strict order of sequence. The drawingsshowing embodiments of the invention are semi-diagrammatic and not toscale and, particularly, some of the dimensions are for the clarity ofpresentation and are shown exaggerated in the drawing Figures.Similarly, although the views in the drawings for the ease ofdescription generally show similar orientations, this depiction in theFigures is arbitrary for the most part. Generally, the invention can beoperated in any orientation.

Notation and Nomenclature:

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present invention,discussions utilizing terms such as “processing” or “accessing” or“executing” or “storing” or “rendering” or the like, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories and other computer readable media into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices. When a component appears in several embodiments, the use of thesame reference numeral signifies that the component is the samecomponent as illustrated in the original embodiment.

Radiation Therapy Planning Using Integrated Model

FIG. 1 is a function block diagram illustrating an exemplaryconfiguration of an automated therapy planning system 100 including acollection of predictive models 121 and a model selection module 122 inaccordance with an embodiment of the present disclosure. The system 100includes an input interface 110 to receive patient data, a dataprocessing component 120 that implements an integrated model andincludes a collection of predictive sub-models 121, a model selectionmodule 122 and prediction generation module 125, and an output interface130. The system 100 in whole or in part may be implemented as a softwareprogram, hardware logic, or a combination thereof.

Each predictive sub-model in the collection 121 may only be applicableto limited regions with respect to the patient data features, e.g.,geometric parameters, contained in the patient data 101. For instance,depending on the applicable regions encompassed by the training data, asub-model may be determined to be valid to predict for high riskprostate cancer with a tumor size within a certain range; while anothersub-model may be determined to be effective to predict for low riskprostate cancer with a tumor size within another range. The individualpredictive models are combined into an integrated model capable ofautomatically selecting an applicable sub-model for a specific set ofpatient data, as will be described in greater detail below.

During operation, based on the patient data 101 provided through theinput interface 110, the model selection module 122 can automaticallyselect a suitable sub-model from the collection of sub-models 121. Theselected sub-model is used to generate a prediction at the predictiongeneration module 123 and output the results through the outputinterface 130. By virtue of an automatic sub-model selection processthat may be transparent to a user, e.g., a therapy planner, theintegrated model advantageously can cover extended effective regions fortherapy prediction without the need for manual selection of a suitablesub-model. In some embodiments, the system may comprise a user interfacethat allows a user to narrow down the sub-model search scope of byuser-defined constraints. A set of patient data typically includesmultiple data points. As will be appreciated by those skilled in theart, the present disclosure is not limited to any mechanism or criteriaof determining a matching sub-model based on patient data. For example,a sub-model may be selected because a predetermined number of points ofthe patient data fall in the effective regions of the sub-model. In someembodiments, each sub-model has its own training set, e.g., a set ofpre-treated patient cases), the matching criteria for anew patient casemay be certain similarity metric between the new single case and thetraining set. For example, a caparison between parameters of the newpatient case and the mean value of the same parameters in the trainingset can be used as a similarity metric. The selection could be based onmaximum similarity, e.g., only one sub-model is selected, the one withthe highest score in the chosen similarity metrics. The sub-modelselection could also be based on certain acceptable similarity level,e.g., the number of selected cub-model could differ, when all sub-modelswith high enough similarity metric s core are selected.

The input patient data may contain any combination of parameters thatcan practically affect the therapy in a manner that is well known in theart. For example, the patient data may be organized as a vector or adata structure comprising feature elements of target size, organ at risksizes organ shape descriptions, partial target volumes overlapping oneorgan, partial target volumes overlapping multiple organs, partial organvolumes overlapping the target, partial organ volumes overlapping otherorgans, and etc.

As will be appreciated by those skilled in the art, the presentdisclosure is not limited to any specific mechanism of generatingindividual sub-models or any specific collection of sub-models. Forexample, the sub-models encompassed by an integrated model in accordancewith the present disclosure may be resulted from any curve fittingtechnique that is well known in the art. Specifically, the curve fittingmay be based on a regression analysis such as linear regression,interpolation, or non-linear regression, e.g., major axis, reduced majoraxis, polynomial, exponential, logarithmic power, and etc.

The individual sub-models may originate from a clinic having severalmodels to cover different regions, or developed by radiation equipmentprovider, or are shared among several clinics. The models may be derivedfrom published literature data or clinical data as submitted by clinicpractitioners. As will be appreciated by those skilled in the art, themodels may be shared either without or without providing the actualpatient data related to the training set used to train the model. Themodels may be used without accessing he original patient data used fortraining. In some embodiments, a certain clinic or other model providercan simultaneously create all sub-models to be used together, orproduces an integrated model by combining several previously trainingsub-models together. In the latter case, some or all sub-models may havebeen obtaining from other parties. In some embodiments, the sub-modeltraining can be done based on training set in a cloud. In someembodiments, several sub-models are made available in a cloud and asoftware component may be used to search a suitable sub-model from thecloud.

The present disclosure may be applied in association with any type ofradiation therapy in conjunction with any type of radiation therapyequipment, such as intensity modulated radiation therapy (IMRT), photontherapy, charged particle therapy, and etc. As will be appreciated bythose skilled in the art, the present disclosure is not limited to anyspecific type of results that can be derived from an integrated modeland individual sub-models. The planning system can also produce, forexample, dose predictions to the tumor and normal tissue achieved by aradiation therapy system, or corresponding operating parameters of aparticular radiation therapy equipment. For example, in radiationtherapy planning for IMRT, the objective is usually to estimate a set ofparameters to control a radiation therapy device for deliveringradiation to a patient. Such a system may also predict chance ofsurvival, or chance of complication. The output may be use to feed to adownstream optimization system prior to use according to anyoptimization technique that is well known in the art.

FIG. 2 is a flow chart illustrating an exemplary method 200 of automatictherapy planning in accordance with an embodiment of the presentdisclosure. Method 200 may be implemented as a system illustrated inFIG. 1. At 201, a set of new patient data is received to a system thatemploys an integrated sub-model. At 202, one or more suitable sub-modelscan be automatically selected by the integrated model based on thereceived patient data. The selected sub-model can be used to process thenew patient data at 203 and output a set of therapy planning parametersat 204.

FIG. 3A is a diagram illustrating an exemplary configuration of anintegrated model operable to determine a suitable sub-model by virtue ofevaluation in accordance with an embodiment of the present disclosure.In this example, the integrated model comprises a two-level hierarchicalstructure. The first level is a model selection component. The secondlevel is a collection of sub-models 320, e.g., M1-M8, with limitedapplication regions with respect to the parameters contained in thepatient data. The patient data 301 is provided to the predictive models320 and processed thereby. In some embodiments, regardless of therespective applicable regions, each sub-model can yield a predictionresult, e.g., achievable dose distribution prediction, accompanied withparameters indicative of the quality of the prediction, such asreliability of the result, e.g., affected by the internal coherence ofthe training set, complexity of the predicted plan, and probability ofthe result. In some other embodiments, data indicating reliability of asub-model based on comparison between the prediction made by thesub-model and the actual outcome of the treatment by use of theprediction its can be included in the evaluation metrics.

The outputs of the predictive models M1-M8 are furnished to theprediction evaluation component 330 that is configured tocomprehensively evaluate the corresponding prediction results and thequality indicators in accordance with predefined criteria or a rankingmechanism. Thereby, the model selection module 310 can output theresults generated by the predictive models that meet the predefinedcriteria. It will be appreciated by those skilled in the art that thepresent disclosure is not limited to any specific evaluation measure.

FIG. 3B is a flow chart depicting an exemplary method 350 of automatictherapy planning by employing an integrated model in which a suitablepredictive sub-model can be determined by virtue of evaluation inaccordance with an embodiment of the present disclosure. A respectivepredictive model contained in the integrated model processes the inputpatient data at 351 and generates a prediction result at 352. At 353,the sub-model can also generate a parameter representing reliability ofthe prediction result with respect to the patient data. The processdescribed in 351-353 is repeated for all sub-models or a subset ofmodels in the integrated model, which can be performed eithersimultaneously or sequentially. If performed one at a time, it may bethat all sub-models are evaluated or some sub-set of them. The resultsare then evaluated comprehensively with respect to the input patientdata at 354 in accordance with predetermined criteria. An automatedevaluation process may include determining the quality (e.g., accuracyor reliability) of the prediction. It can also be based on tryingautomatic planning based on automatically generated instructions to planoptimizer (e.g., automatically generated optimization objectives), andevaluating the clinical quality of the automatically created plans, At355, a resultant prediction plan can be selected based on the evaluationand may be used for further evaluation. As described above, parametersindicative of the quality of the prediction, such as reliability of theresult based on comparison between the prediction made by the sub-modeland the actual outcome of the treatment by use of the prediction,complexity of the predicted plan, and probability of the result can allbe incorporated for the evolution process. The evaluation criteria maybe determined by radiation therapy experts for example.

FIG. 4A is a diagram illustrating an exemplary configuration of anintegrated model 400 including a hierarchy of decision levels forpredictive model selection in accordance with an embodiment of thepresent disclosure. In the illustrated embodiment, the integrated modelincludes a collection of models 420 at the bottom level and a pluralityof decision levels, e.g., A, B, C, each corresponding to a respectivefeature set in the patient data and each feature set corresponding toone or more features in the patient data. Each decision level includesmultiple predefined divisions, or categories, of a respective featureset. For example, level A may correspond to feature F1, e.g., the typeof organ to be treated with A1 representing prostate and A2 representinghead-neck. Level B may correspond to F2, e.g., size of target, with B1and B3 representing a range of less than 3 mm, and B2 and B4representing a range of equal to or greater than 3 mm. As shown, adivision in a subordinate decision level is nested with a division in asuperordinate decision level, e.g., B1 and B2 are nested with A1, and C1and C2 are nested with B1.

Provided with patient data 401, e.g., with feature sets F1, F2 and F3,the model selection component 410 can identify one or more applicabledivisions from each level based on the patient data of each correspondfeature set, starting from the top level. Then one or more predictivemodel can be selected based on the identified applicable divisions.

FIG. 4B is a flow charge depicting an exemplary method 450 of selectinga suitable predictive model in a hierarchical model in accordance withan embodiment of the present disclosure. The hierarchical model may beimplemented as illustrated in FIG. 4B. At 451, a first category isidentified on the top level based on the patient data of the firstfeature corresponding to the top level. At 452, a second category isidentified on the second level based on the patient data of the secondfeature corresponding to the second level. The second category is nestedwith the first category. At 453, a third category is identified on thethird level based on the patient data of the second featurecorresponding to the third level. The third category is nested with thethird category. At 454, a suitable predictive model can be identifiedthat is applicable in all the identified categories.

In some embodiments more than one suitable predictive model may beidentified in a similar process as described with reference to FIG. 4B,and the resulted multiple predictions can be evaluated as described withreference to FIG. 3B.

The hierarchical model in accordance with the present disclosure mayeither be constructed manually or automatically combining existingconfigured predictive models, or automatically configuring a set ofsub-models from a single training set. For example, a large training setcould first be divided into subsets using a clustering algorithm andthen each subset would be used as a training set for regression model.

FIG. 5 is a function block diagram illustrating an automated integratedmodel generation system 500 in accordance with an embodiment of thepresent disclosure. The integrated model generation system 500 includesan input interface 503, a training data classification module 510, asub-model generation module 520, an integration module 530, an inputinterface generation module 541, an output interface generation module541, and an output interface 504. During operation, the system 500 canprocess the training data 501 received at the input interface 503,generate an integrated model, and output the integrated model 502through the output interface 504. The integrated model integrates theplurality of predictive models and is operable to select one or morepredictive sub-model based on a particular set of patient data, e.g.,new patient data, and outcome a therapy prediction. The sub-models in anintegrated model may or may not have internal hierarchy regarding therelatedness among the sub-models. For example, all sub-models may beserial to each other, and thus, no internal non-trivial hierarchy ispresent.

The training data classification module 510 is capable of classifyingtraining data 501 into subsets of training data in accordance with aclustering algorithm, e.g., a hierarchical clustering algorithm. Eachsubset of training data is then provided to the sub-model generationmodule 520 to automatically generate a sub-model in accordance with anysuitable means that is well known in the art. The predictive models arethen provided to the integration module 530 and combined into anintegrated or hierarchical model in accordance with an embodiment of thepresent disclosure. The interface generation modules 541 and 542 cangenerate an input interface and output interface respectively.

In some embodiments, an integrated model may be enlarged incrementallyby configuring new predictive models into a bit different regions. Itmay also be used to create a prediction scheme for reducing region.

FIG. 6 is a flow chart depicting an exemplary method 600 ofautomatically generating an integrated model for therapy planning inaccordance with an embodiment of the present disclosure. At 601, a setof training data is received. At 602, the training data is classifiedinto a plurality of subsets of data based on features in the trainingdata in accordance with a hierarchical clustering algorithm, or anyother suitable algorithm that is well known in the art. In someembodiments, one case in the training set may be part of only onesub-set, e.g., in a hierarchical integrated model. At 603, a predictivesub-model is trained based on each subset of the training data andoperable to generate a radiation treatment prediction, such as inaccordance with a regression analysis technique, or in accordance withany other data fit technique that is well known in the art. At 604, thetrained predictive models are integrated into a hierarchical model witheach level corresponding to a feature set. At 604, the integrated modelis output. As will be appreciated by those skilled in the art, atraining process generally can be may be used to calibrate certaintheoretical and general model to provide as good match as possible inthe training set between the prediction and the actual dosimetricoutcome. During a training process, a set of selected cases (separatepatient data), the training set, may be used to define values of thefree parameters of the model, or a model may be created from previouslyknown cases. Once a model is trained, it becomes a particular model thatcan be used to give reliable predictions to a new patient case, e.g.,with unknown dosimetric outcome.

FIG. 7 is a block diagram illustrating an exemplary computing system 700including an automatic integrated model generator 710 and an automatictherapy plan generator 720 in accordance with an embodiment of thepresent disclosure. The computing system 700 comprises a processor 701,a system memory 702, a GPU 703, I/O interfaces 704 and network circuits705, an operating system 706 and application software 707 including anautomatic integrated model generator 710 and an automatic therapy plangenerator 720 stored in the memory 702. In some other embodiments, anautomatic integrated model generator and an automatic therapy plangenerator can be implemented in two separate systems.

When incorporating the input and configuration input, e.g. trainingdata, and executed by the CPU 701, the automatic integrated modelgenerator 710 can automatically generate an integrated modelencompassing a plurality of predictive models trained by the trainingdata in accordance with an embodiment of the present disclosure. Theautomatic integrated model generator 710 may perform various otherfunctions as discussed in details with reference to FIG. 5 and FIG. 6.

When incorporating the input and configuration input, e.g. a set ofpatient data, and executed by the CPU 701, the automatic therapy plangenerator 720 can automatically generate treatment planning parametersby an automatically selected predictive model in accordance with anembodiment of the present disclosure. The automatic therapy plangenerator 720 may perform various other functions as discussed indetails with reference to FIG. 1, 2, 3A, 3B, 4A and 4B.

As will be appreciated by those with ordinary skill in the art, theautomatic integrated model generator 710 and the automatic therapy plangenerator 720 can be a software implemented in any one or more suitableprogramming languages that are known to those skilled in the art, suchas C, C++, Java, Python, Perl, C#, SQL, etc.

Although certain preferred embodiments and methods have been disclosedherein, it will be apparent from the foregoing disclosure to thoseskilled in the art that variations and modifications of such embodimentsand methods may be made without departing from the spirit and scope ofthe invention. It is intended that the invention shall be limited onlyto the extent required by the appended claims and the rules andprinciples of applicable law.

What is claimed is:
 1. A computer implemented method of automaticallygenerating a radiation treatment plan for a patient, said methodcomprising: accessing patient information; accessing an integrated modelthat integrates a plurality of predictive models, wherein a respectivepredictive model of said plurality of predictive models correlates inputvariables of patient information features with output variablespertinent to radiation treatment planning; automatically selecting oneor more predictive models based on said patient information inaccordance with said integrated model; processing said patientinformation in accordance with said one or more predictive models; andoutputting a resultant radiation treatment prediction.
 2. The computerimplemented method of claim 1, wherein said automatically selecting andsaid processing comprise: generating radiation treatment predictionsbased on said patient information using said plurality of predictivemodels; evaluating said radiation treatment predictions; and selectingsaid one or more predictive models based on said evaluating.
 3. Thecomputer implemented method of claim 2, wherein said evaluatingcomprises evaluating parameters representing reliability, complexity,and probability with respect to said radiation treatment predictions. 4.The computer implemented method of claim 1, wherein said patientinformation comprises data of a plurality of sets of features, each setof said plurality of sets comprising one or more features, and whereineach of said plurality of predictive models is associated with arespective division with respect to each set of features of saidplurality of sets of features, and wherein said automatically selectingcomprises: identifying a corresponding division in each set of featuresbased on said patient information; and selecting said one or morepredictive models based on identified divisions of said plurality ofsets of features.
 5. The computer implemented method of claim 4, whereinsaid integrated model is a hierarchical model comprising a plurality ofintermediate levels and a lowest level, wherein said lowest levelcomprises said plurality of predictive models, wherein each intermediatelevel corresponds to divisions with respect to a respective set offeatures, and wherein divisions corresponding to a subordinateintermediate level are nested with divisions corresponding to asuperordinate intermediate level.
 6. The computer implemented method ofclaim 1, wherein said integrated model is generated by automaticallyclassifying said plurality of predictive models based on previousclinical data in accordance with a clustering algorithm.
 7. The computerimplemented method of claim 4, wherein said plurality of sets offeatures are selected from a group consisting of organ type, organdimension descriptions, target location, target size, geometriccharacterizations of one or more organs at risk proximate to a targetvolume.
 8. The computer implemented method of claim 1, wherein each ofsaid plurality of predictive models is automatically established througha machine training process in accordance with a regression method andbased on previous clinical data.
 9. The computer implemented method ofclaim 1, wherein said resultant radiation treatment prediction comprisesa predicted achievable dose distribution.
 10. A non-transitorycomputer-readable storage medium embodying instructions that, whenexecuted by a processing device, cause the processing device to performa method of automatically generating an integrated model for planning aradiation therapy, said method comprising: accessing a plurality ofpredictive models, wherein each of said plurality of predictive modelscorrelates input variables representing patient information featureswith variables pertinent to radiation treatment planning; andintegrating said plurality of predictive models into an integratedmodel, wherein said integrated model is configured to: receive data ofsaid patient information features; automatically select one or morepredictive models from said plurality of predictive models based on saiddata of said patient information features; and process said data of saidpatient information features in accordance with said one or morepredictive models to output a resultant radiation treatment prediction.11. The non-transitory computer-readable storage medium of claim 10,wherein the method further comprises: configuring an input interfaceoperable to receive said data of said patient information features; andconfiguring an output interface operable to output said resultantradiation treatment prediction.
 12. The non-transitory computer-readablestorage medium of claim 10, wherein said integrating comprisesassociating an applicable category with each of said patient informationfeatures for each of said plurality of predictive models.
 13. Thenon-transitory computer-readable storage medium of claim 12, whereinsaid integrating comprises integrating said plurality of predictivemodels into a hierarchy comprising a bottom level and one or moredecision levels, wherein said bottom level comprises said plurality ofpredictive models, and wherein said one or more decision levels areoperable to automatically select said one or more predictive models fromsaid bottom level based on applicable categories associated with saiddata of said patient information features.
 14. The non-transitorycomputer-readable storage medium of claim 10, wherein said methodfurther comprises: accessing training data; and generating saidplurality of predictive models based on said training data in a machinetraining process, wherein a respective predictive model of saidplurality of predictive models is generated in accordance with analgorithm selected from a group consisting of a linear regressionalgorithm, a classification algorithm, a decision tree algorithm, asegmentation algorithm, an association algorithm, a sequence clusteringalgorithm, and a combination thereof.
 15. The non-transitorycomputer-readable storage medium of claim 14, wherein said methodfurther comprises classifying said training data into subsets oftraining data in accordance with a clustering algorithm, and whereinsaid generating said plurality of predictive models comprisesconfiguring a predictive model based on a subset of training data. 16.The non-transitory computer-readable storage medium of claim 11, whereinsaid patient information features are selected from a group consistingof organ identification, organ dimension descriptions, target location,target size, and geometric characterizations of one or more organs atrisk proximate to a target volume.
 17. A system comprising: a processor;a memory coupled to said processor and comprising instructions that,when executed by said processor, cause the processor to perform a methodof automatically generating a radiation therapy plan, said methodcomprising: accessing patient information; accessing an integrated modelthat integrates a plurality of predictive models, wherein a respectivepredictive model of said plurality of predictive models correlates inputvariables of patient information features with output variablespertinent to radiation treatment planning; automatically selecting oneor more predictive models based on said patient information inaccordance with said integrated model; processing said patientinformation in accordance with said one or more predictive models; andoutputting a resultant radiation treatment prediction.
 18. The system ofclaim 17, wherein said automatically selecting and said processingcomprise: generating radiation treatment predictions based on saidpatient information using said plurality of predictive models;evaluating said radiation treatment predictions; and selecting said oneor more predictive models based on said evaluating.
 19. The system ofclaim 17, wherein said patient information comprises data of a pluralityof sets of features, each set of said plurality of sets comprising oneor more features, and wherein each of said plurality of predictivemodels is associated with a respective division with respect to each setof features of said plurality of sets of features, and wherein saidautomatically selecting comprises: identifying a corresponding divisionin each set of features based on said patient information; and selectingsaid one or more predictive models based on identified divisions of saidplurality of sets of features.
 20. The system of claim 19, wherein saidplurality of sets of features are selected from a group consisting oforgan type, organ dimension descriptions, target location, target size,geometric characterizations of one or more organs at risk proximate to atarget volume, and wherein said resultant radiation treatment predictioncomprises a predicted achievable dose distribution.